Software Design Document (SDD)

Version: 1.0 Date: October 08, 2025 SPDX-License-Identifier: BSD-3-Clause License File: See the LICENSE file in the project root. Copyright: © 2025 Michael Gardner, A Bit of Help, Inc. Authors: Michael Gardner, Claude Code Status: Active

1. Introduction

1.1 Purpose

This Software Design Document (SDD) describes the architectural and detailed design of the Adaptive Pipeline system. It provides technical specifications for developers implementing, maintaining, and extending the system.

Intended Audience:

Software developers implementing features
Technical architects reviewing design decisions
Code reviewers evaluating pull requests
New team members onboarding to the codebase

1.2 Scope

This document covers the design of a high-performance file processing pipeline implemented in Rust, following Domain-Driven Design (DDD), Clean Architecture, and Hexagonal Architecture principles.

Covered Topics:

System architecture and layer organization
Component interactions and dependencies
Data structures and algorithms
Interface specifications
Design patterns and rationale
Technology stack and tooling
Cross-cutting concerns

Not Covered:

Requirements (see SRS)
Testing strategies (see Test Plan)
Deployment procedures
Operational procedures

1.3 Design Philosophy

Core Principles:

Domain-Driven Design: Business logic isolated in domain layer
Clean Architecture: Dependencies point inward toward domain
Hexagonal Architecture: Ports and adapters isolate external concerns
SOLID Principles: Single responsibility, dependency inversion
Rust Idioms: Zero-cost abstractions, ownership, type safety

Design Goals:

Correctness: Type-safe, compiler-verified invariants
Performance: Async I/O, parallel processing, minimal allocations
Maintainability: Clear separation of concerns, testable components
Extensibility: Plugin architecture for custom stages
Observability: Comprehensive metrics and logging

1.4 References

Software Requirements Specification
Architecture Overview
Domain Model
Clean Architecture: Robert C. Martin, 2017
Domain-Driven Design: Eric Evans, 2003

2. System Architecture

2.1 Architectural Overview

The system follows a layered architecture with strict dependency rules:

┌─────────────────────────────────────────────┐
│         Presentation Layer                  │ ← CLI, Future APIs
│  (bootstrap, cli, config, logger)           │
├─────────────────────────────────────────────┤
│         Application Layer                    │ ← Use Cases
│  (commands, services, orchestration)         │
├─────────────────────────────────────────────┤
│         Domain Layer (Core)                  │ ← Business Logic
│  (entities, value objects, services)         │
├─────────────────────────────────────────────┤
│         Infrastructure Layer                 │ ← External Concerns
│  (repositories, adapters, file I/O)          │
└─────────────────────────────────────────────┘

Dependency Rule: Inner layers know nothing about outer layers

2.2 Layered Architecture Details

2.2.1 Presentation Layer (Bootstrap Crate)

Responsibilities:

CLI argument parsing and validation
Application configuration
Logging and console output
Exit code handling
Platform-specific concerns

Key Components:

cli::parser - Command-line argument parsing
cli::validator - Input validation and sanitization
config::AppConfig - Application configuration
logger::Logger - Logging abstraction
platform::Platform - Platform detection

Design Decisions:

Separate crate for clean separation
No domain dependencies
Generic, reusable components

2.2.2 Application Layer

Responsibilities:

Orchestrate use cases
Coordinate domain services
Manage transactions
Handle application-level errors

Key Components:

use_cases::ProcessFileUseCase - File processing orchestration
use_cases::RestoreFileUseCase - File restoration orchestration
commands::ProcessFileCommand - Command pattern implementation
commands::RestoreFileCommand - Restoration command

Design Patterns:

Command Pattern: Encapsulate requests as objects
Use Case Pattern: Application-specific business rules
Dependency Injection: Services injected via constructors

Key Abstractions:

#![allow(unused)]
fn main() {
#[async_trait]
pub trait UseCase<Input, Output> {
    async fn execute(&self, input: Input) -> Result<Output>;
}
}

2.2.3 Domain Layer

Responsibilities:

Core business logic
Domain rules and invariants
Domain events
Pure functions (no I/O)

Key Components:

Entities:

Pipeline - Aggregate root for pipeline configuration
PipelineStage - Individual processing stage
FileMetadata - File information and state

Value Objects:

ChunkSize - Validated chunk size
PipelineId - Unique pipeline identifier
StageType - Type-safe stage classification

Domain Services:

CompressionService - Compression/decompression logic
EncryptionService - Encryption/decryption logic
ChecksumService - Integrity verification logic
FileIOService - File operations abstraction

Invariants Enforced:

#![allow(unused)]
fn main() {
impl Pipeline {
    pub fn new(name: String, stages: Vec<PipelineStage>) -> Result<Self> {
        // Invariant: Name must be non-empty
        if name.trim().is_empty() {
            return Err(PipelineError::InvalidName);
        }

        // Invariant: At least one stage required
        if stages.is_empty() {
            return Err(PipelineError::NoStages);
        }

        // Invariant: Stages must have unique, sequential order
        validate_stage_order(&stages)?;

        Ok(Pipeline { name, stages })
    }
}
}

2.2.4 Infrastructure Layer

Responsibilities:

External system integration
Data persistence
File I/O operations
Third-party library adapters

Key Components:

Repositories:

SqlitePipelineRepository - SQLite-based persistence
InMemoryPipelineRepository - Testing/development

Adapters:

BrotliCompressionAdapter - Brotli compression
ZstdCompressionAdapter - Zstandard compression
AesGcmEncryptionAdapter - AES-256-GCM encryption
Sha256ChecksumAdapter - SHA-256 hashing

File I/O:

BinaryFileWriter - .adapipe format writer
BinaryFileReader - .adapipe format reader
ChunkedFileReader - Streaming file reader
ChunkedFileWriter - Streaming file writer

2.3 Hexagonal Architecture (Ports & Adapters)

Primary Ports (driving adapters - how the world uses us):

CLI commands
Future: REST API, gRPC API

Secondary Ports (driven adapters - how we use the world):

#![allow(unused)]
fn main() {
// Domain defines the interface (port)
#[async_trait]
pub trait CompressionPort: Send + Sync {
    async fn compress(&self, data: &[u8]) -> Result<Vec<u8>>;
    async fn decompress(&self, data: &[u8]) -> Result<Vec<u8>>;
}

// Infrastructure provides implementations (adapters)
pub struct BrotliAdapter {
    quality: u32,
}

#[async_trait]
impl CompressionPort for BrotliAdapter {
    async fn compress(&self, data: &[u8]) -> Result<Vec<u8>> {
        // Brotli-specific implementation
    }
}
}

Benefits:

Domain layer testable in isolation
Easy to swap implementations
Third-party dependencies isolated
Technology-agnostic core

3. Component Design

3.1 Pipeline Processing Engine

Architecture:

┌──────────────┐
│   Pipeline   │ (Aggregate Root)
│              │
│  - name      │
│  - stages[]  │
│  - metadata  │
└──────┬───────┘
       │ has many
       ▼
┌──────────────┐
│PipelineStage │
│              │
│  - name      │
│  - type      │
│  - config    │
│  - order     │
└──────────────┘

Processing Flow:

#![allow(unused)]
fn main() {
pub struct PipelineProcessor {
    context: Arc<PipelineContext>,
}

impl PipelineProcessor {
    pub async fn process(&self, pipeline: &Pipeline, input: PathBuf)
        -> Result<PathBuf>
    {
        // 1. Validate input file
        let metadata = self.validate_input(&input).await?;

        // 2. Create output file
        let output = self.create_output_file(&pipeline.name).await?;

        // 3. Process in chunks
        let chunks = self.chunk_file(&input, self.context.chunk_size).await?;

        // 4. Execute stages on each chunk (parallel)
        for chunk in chunks {
            let processed = self.execute_stages(pipeline, chunk).await?;
            self.write_chunk(&output, processed).await?;
        }

        // 5. Write metadata and finalize
        self.finalize(&output, metadata).await?;

        Ok(output)
    }

    async fn execute_stages(&self, pipeline: &Pipeline, chunk: Chunk)
        -> Result<Chunk>
    {
        let mut data = chunk.data;

        for stage in &pipeline.stages {
            data = match stage.stage_type {
                StageType::Compression => {
                    self.context.compression.compress(&data).await?
                }
                StageType::Encryption => {
                    self.context.encryption.encrypt(&data).await?
                }
                StageType::Checksum => {
                    let hash = self.context.checksum.hash(&data).await?;
                    self.context.store_hash(chunk.id, hash);
                    data // Checksum doesn't transform data
                }
                StageType::PassThrough => data,
            };
        }

        Ok(Chunk { data, ..chunk })
    }
}
}

3.2 Chunk Processing Strategy

Design Rationale:

Large files cannot fit in memory
Streaming processing enables arbitrary file sizes
Parallel chunk processing utilizes multiple cores
Fixed chunk size simplifies memory management

Implementation:

#![allow(unused)]
fn main() {
pub const DEFAULT_CHUNK_SIZE: usize = 1_048_576; // 1 MB

pub struct Chunk {
    pub id: usize,
    pub data: Vec<u8>,
    pub is_final: bool,
}

pub struct ChunkedFileReader {
    file: File,
    chunk_size: usize,
    chunk_id: usize,
}

impl ChunkedFileReader {
    pub async fn read_chunk(&mut self) -> Result<Option<Chunk>> {
        let mut buffer = vec![0u8; self.chunk_size];
        let bytes_read = self.file.read(&mut buffer).await?;

        if bytes_read == 0 {
            return Ok(None);
        }

        buffer.truncate(bytes_read);

        let chunk = Chunk {
            id: self.chunk_id,
            data: buffer,
            is_final: bytes_read < self.chunk_size,
        };

        self.chunk_id += 1;
        Ok(Some(chunk))
    }
}
}

Parallel Processing:

#![allow(unused)]
fn main() {
use tokio::task::JoinSet;

pub async fn process_chunks_parallel(
    chunks: Vec<Chunk>,
    stage: &dyn ProcessingStage,
) -> Result<Vec<Chunk>> {
    let mut join_set = JoinSet::new();

    for chunk in chunks {
        let stage = Arc::clone(&stage);
        join_set.spawn(async move {
            stage.process(chunk).await
        });
    }

    let mut results = Vec::new();
    while let Some(result) = join_set.join_next().await {
        results.push(result??);
    }

    // Sort by chunk ID to maintain order
    results.sort_by_key(|c| c.id);

    Ok(results)
}
}

3.3 Repository Pattern Implementation

Interface (Port):

#![allow(unused)]
fn main() {
#[async_trait]
pub trait PipelineRepository: Send + Sync {
    async fn save(&self, pipeline: &Pipeline) -> Result<()>;
    async fn find_by_id(&self, id: &str) -> Result<Option<Pipeline>>;
    async fn find_by_name(&self, name: &str) -> Result<Option<Pipeline>>;
    async fn delete(&self, id: &str) -> Result<()>;
    async fn list_all(&self) -> Result<Vec<Pipeline>>;
}
}

SQLite Implementation:

#![allow(unused)]
fn main() {
pub struct SqlitePipelineRepository {
    pool: Arc<SqlitePool>,
}

#[async_trait]
impl PipelineRepository for SqlitePipelineRepository {
    async fn save(&self, pipeline: &Pipeline) -> Result<()> {
        let mut tx = self.pool.begin().await?;

        // Insert pipeline
        sqlx::query!(
            "INSERT INTO pipelines (id, name, created_at) VALUES (?, ?, ?)",
            pipeline.id,
            pipeline.name,
            pipeline.created_at
        )
        .execute(&mut *tx)
        .await?;

        // Insert stages
        for stage in &pipeline.stages {
            sqlx::query!(
                "INSERT INTO pipeline_stages
                 (pipeline_id, name, type, order_num, config)
                 VALUES (?, ?, ?, ?, ?)",
                pipeline.id,
                stage.name,
                stage.stage_type.to_string(),
                stage.order,
                serde_json::to_string(&stage.configuration)?
            )
            .execute(&mut *tx)
            .await?;
        }

        tx.commit().await?;
        Ok(())
    }
}
}

3.4 Adapter Pattern for Algorithms

Design: Each algorithm family has a common interface with multiple implementations.

Compression Adapters:

#![allow(unused)]
fn main() {
#[async_trait]
pub trait CompressionAdapter: Send + Sync {
    fn name(&self) -> &str;
    async fn compress(&self, data: &[u8]) -> Result<Vec<u8>>;
    async fn decompress(&self, data: &[u8]) -> Result<Vec<u8>>;
}

pub struct BrotliAdapter {
    quality: u32,
}

#[async_trait]
impl CompressionAdapter for BrotliAdapter {
    fn name(&self) -> &str { "brotli" }

    async fn compress(&self, data: &[u8]) -> Result<Vec<u8>> {
        let mut output = Vec::new();
        let mut compressor = brotli::CompressorReader::new(
            data,
            4096,
            self.quality,
            22
        );
        compressor.read_to_end(&mut output)?;
        Ok(output)
    }

    async fn decompress(&self, data: &[u8]) -> Result<Vec<u8>> {
        let mut output = Vec::new();
        let mut decompressor = brotli::Decompressor::new(data, 4096);
        decompressor.read_to_end(&mut output)?;
        Ok(output)
    }
}
}

Factory Pattern:

#![allow(unused)]
fn main() {
pub struct AdapterFactory;

impl AdapterFactory {
    pub fn create_compression(
        algorithm: &str
    ) -> Result<Box<dyn CompressionAdapter>> {
        match algorithm.to_lowercase().as_str() {
            "brotli" => Ok(Box::new(BrotliAdapter::new(11))),
            "zstd" => Ok(Box::new(ZstdAdapter::new(3))),
            "gzip" => Ok(Box::new(GzipAdapter::new(6))),
            "lz4" => Ok(Box::new(Lz4Adapter::new())),
            _ => Err(PipelineError::UnsupportedAlgorithm(
                algorithm.to_string()
            )),
        }
    }
}
}

4. Data Design

4.1 Domain Entities

Pipeline Entity:

#![allow(unused)]
fn main() {
pub struct Pipeline {
    id: PipelineId,
    name: String,
    stages: Vec<PipelineStage>,
    created_at: DateTime<Utc>,
    updated_at: DateTime<Utc>,
}

impl Pipeline {
    // Factory method enforces invariants
    pub fn new(name: String, stages: Vec<PipelineStage>) -> Result<Self> {
        // Validation logic
    }

    // Domain behavior
    pub fn add_stage(&mut self, stage: PipelineStage) -> Result<()> {
        // Ensure stage order is valid
        // Ensure no duplicate stage names
    }

    pub fn execute_order(&self) -> &[PipelineStage] {
        &self.stages
    }

    pub fn restore_order(&self) -> Vec<PipelineStage> {
        self.stages.iter().rev().cloned().collect()
    }
}
}

PipelineStage Entity:

#![allow(unused)]
fn main() {
pub struct PipelineStage {
    name: String,
    stage_type: StageType,
    configuration: StageConfiguration,
    order: usize,
}

pub enum StageType {
    Compression,
    Encryption,
    Checksum,
    PassThrough,
}

pub struct StageConfiguration {
    pub algorithm: String,
    pub parameters: HashMap<String, String>,
    pub parallel_processing: bool,
    pub chunk_size: Option<usize>,
}
}

4.2 Value Objects

ChunkSize:

#![allow(unused)]
fn main() {
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub struct ChunkSize(usize);

impl ChunkSize {
    pub const MIN: usize = 1024;  // 1 KB
    pub const MAX: usize = 100 * 1024 * 1024;  // 100 MB
    pub const DEFAULT: usize = 1_048_576;  // 1 MB

    pub fn new(size: usize) -> Result<Self> {
        if size < Self::MIN || size > Self::MAX {
            return Err(PipelineError::InvalidChunkSize {
                size,
                min: Self::MIN,
                max: Self::MAX
            });
        }
        Ok(ChunkSize(size))
    }

    pub fn value(&self) -> usize {
        self.0
    }
}
}

4.3 Database Schema

Schema Management: Using sqlx with migrations

-- migrations/20250101000000_initial_schema.sql

-- Pipelines table: stores pipeline configurations
CREATE TABLE IF NOT EXISTS pipelines (
    id TEXT PRIMARY KEY,
    name TEXT NOT NULL UNIQUE,
    archived BOOLEAN NOT NULL DEFAULT false,
    created_at TEXT NOT NULL,
    updated_at TEXT NOT NULL
);

-- Pipeline configuration: key-value pairs for pipeline settings
CREATE TABLE IF NOT EXISTS pipeline_configuration (
    pipeline_id TEXT NOT NULL,
    key TEXT NOT NULL,
    value TEXT NOT NULL,
    archived BOOLEAN NOT NULL DEFAULT FALSE,
    created_at TEXT NOT NULL,
    updated_at TEXT NOT NULL,
    PRIMARY KEY (pipeline_id, key),
    FOREIGN KEY (pipeline_id) REFERENCES pipelines(id) ON DELETE CASCADE
);

-- Pipeline stages: defines processing stages in a pipeline
CREATE TABLE IF NOT EXISTS pipeline_stages (
    id TEXT PRIMARY KEY,
    pipeline_id TEXT NOT NULL,
    name TEXT NOT NULL,
    stage_type TEXT NOT NULL,
    enabled BOOLEAN NOT NULL DEFAULT TRUE,
    stage_order INTEGER NOT NULL,
    algorithm TEXT NOT NULL,
    parallel_processing BOOLEAN NOT NULL DEFAULT FALSE,
    chunk_size INTEGER,
    archived BOOLEAN NOT NULL DEFAULT FALSE,
    created_at TEXT NOT NULL,
    updated_at TEXT NOT NULL,
    FOREIGN KEY (pipeline_id) REFERENCES pipelines(id) ON DELETE CASCADE
);

-- Stage parameters: configuration for individual stages
CREATE TABLE IF NOT EXISTS stage_parameters (
    stage_id TEXT NOT NULL,
    key TEXT NOT NULL,
    value TEXT NOT NULL,
    archived BOOLEAN NOT NULL DEFAULT FALSE,
    created_at TEXT NOT NULL,
    updated_at TEXT NOT NULL,
    PRIMARY KEY (stage_id, key),
    FOREIGN KEY (stage_id) REFERENCES pipeline_stages(id) ON DELETE CASCADE
);

-- Processing metrics: tracks pipeline execution metrics
CREATE TABLE IF NOT EXISTS processing_metrics (
    pipeline_id TEXT PRIMARY KEY,
    bytes_processed INTEGER NOT NULL DEFAULT 0,
    bytes_total INTEGER NOT NULL DEFAULT 0,
    chunks_processed INTEGER NOT NULL DEFAULT 0,
    chunks_total INTEGER NOT NULL DEFAULT 0,
    start_time_rfc3339 TEXT,
    end_time_rfc3339 TEXT,
    processing_duration_ms INTEGER,
    throughput_bytes_per_second REAL NOT NULL DEFAULT 0.0,
    compression_ratio REAL,
    error_count INTEGER NOT NULL DEFAULT 0,
    warning_count INTEGER NOT NULL DEFAULT 0,
    input_file_size_bytes INTEGER NOT NULL DEFAULT 0,
    output_file_size_bytes INTEGER NOT NULL DEFAULT 0,
    input_file_checksum TEXT,
    output_file_checksum TEXT,
    FOREIGN KEY (pipeline_id) REFERENCES pipelines(id) ON DELETE CASCADE
);

-- Indexes for performance
CREATE INDEX IF NOT EXISTS idx_pipeline_stages_pipeline_id ON pipeline_stages(pipeline_id);
CREATE INDEX IF NOT EXISTS idx_pipeline_stages_order ON pipeline_stages(pipeline_id, stage_order);
CREATE INDEX IF NOT EXISTS idx_pipeline_configuration_pipeline_id ON pipeline_configuration(pipeline_id);
CREATE INDEX IF NOT EXISTS idx_stage_parameters_stage_id ON stage_parameters(stage_id);
CREATE INDEX IF NOT EXISTS idx_pipelines_name ON pipelines(name) WHERE archived = false;

4.4 Binary File Format (.adapipe)

Format Specification:

.adapipe File Structure:
┌────────────────────────────────────┐
│ Magic Number (4 bytes): "ADPI"    │
│ Version (2 bytes): Major.Minor     │
│ Header Length (4 bytes)            │
├────────────────────────────────────┤
│ Header (JSON):                     │
│  - pipeline_name                   │
│  - stage_configs[]                 │
│  - original_filename               │
│  - original_size                   │
│  - chunk_count                     │
│  - checksum_algorithm              │
├────────────────────────────────────┤
│ Chunk 0:                           │
│  - Length (4 bytes)                │
│  - Checksum (32 bytes)             │
│  - Data (variable)                 │
├────────────────────────────────────┤
│ Chunk 1: ...                       │
├────────────────────────────────────┤
│ ...                                │
├────────────────────────────────────┤
│ Footer (JSON):                     │
│  - total_checksum                  │
│  - created_at                      │
└────────────────────────────────────┘

Implementation:

#![allow(unused)]
fn main() {
pub struct AdapipeHeader {
    pub pipeline_name: String,
    pub stages: Vec<StageConfig>,
    pub original_filename: String,
    pub original_size: u64,
    pub chunk_count: usize,
    pub checksum_algorithm: String,
}

pub struct BinaryFileWriter {
    file: File,
}

impl BinaryFileWriter {
    pub async fn write_header(&mut self, header: &AdapipeHeader)
        -> Result<()>
    {
        // Magic number
        self.file.write_all(b"ADPI").await?;

        // Version
        self.file.write_u8(1).await?;  // Major
        self.file.write_u8(0).await?;  // Minor

        // Header JSON
        let header_json = serde_json::to_vec(header)?;
        self.file.write_u32(header_json.len() as u32).await?;
        self.file.write_all(&header_json).await?;

        Ok(())
    }

    pub async fn write_chunk(&mut self, chunk: &ProcessedChunk)
        -> Result<()>
    {
        self.file.write_u32(chunk.data.len() as u32).await?;
        self.file.write_all(&chunk.checksum).await?;
        self.file.write_all(&chunk.data).await?;
        Ok(())
    }
}
}

5. Interface Design

5.1 Public API

Command-Line Interface:

# Process a file
pipeline process \
    --input ./document.pdf \
    --output ./document.adapipe \
    --pipeline secure-archive \
    --compress zstd \
    --encrypt aes256gcm \
    --key-file ./key.txt

# Restore a file
pipeline restore \
    --input ./document.adapipe \
    --output ./document.pdf \
    --key-file ./key.txt

# List pipelines
pipeline list

# Create pipeline configuration
pipeline create \
    --name my-pipeline \
    --stages compress:zstd,encrypt:aes256gcm

Future: Library API:

use adaptive_pipeline_domain::entities::{Pipeline, PipelineStage, StageType, StageConfiguration};
use std::collections::HashMap;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create pipeline stages
    let compression_stage = PipelineStage::new(
        "compress".to_string(),
        StageType::Compression,
        StageConfiguration::new(
            "zstd".to_string(),
            HashMap::new(),
            false,
        ),
        0,
    )?;

    let encryption_stage = PipelineStage::new(
        "encrypt".to_string(),
        StageType::Encryption,
        StageConfiguration::new(
            "aes256gcm".to_string(),
            HashMap::new(),
            false,
        ),
        1,
    )?;

    // Create pipeline with stages
    let pipeline = Pipeline::new(
        "my-pipeline".to_string(),
        vec![compression_stage, encryption_stage],
    )?;

    // Process file (future implementation)
    // let processor = PipelineProcessor::new()?;
    // let output = processor.process(&pipeline, "input.txt").await?;

    println!("Pipeline created: {}", pipeline.name());
    Ok(())
}

5.2 Internal APIs

Domain Service Interfaces:

#![allow(unused)]
fn main() {
#[async_trait]
pub trait CompressionService: Send + Sync {
    async fn compress(
        &self,
        algorithm: &str,
        data: &[u8]
    ) -> Result<Vec<u8>>;

    async fn decompress(
        &self,
        algorithm: &str,
        data: &[u8]
    ) -> Result<Vec<u8>>;
}

#[async_trait]
pub trait EncryptionService: Send + Sync {
    async fn encrypt(
        &self,
        algorithm: &str,
        data: &[u8],
        key: &[u8]
    ) -> Result<Vec<u8>>;

    async fn decrypt(
        &self,
        algorithm: &str,
        data: &[u8],
        key: &[u8]
    ) -> Result<Vec<u8>>;
}
}

6. Technology Stack

6.1 Core Technologies

Technology	Purpose	Version
Rust	Primary language	1.75+
Tokio	Async runtime	1.35+
SQLx	Database access	0.7+
Serde	Serialization	1.0+
Anyhow	Error handling	1.0+
Clap	CLI parsing	4.5+

6.2 Algorithms & Libraries

Compression:

Brotli (brotli crate)
Zstandard (zstd crate)
Gzip (flate2 crate)
LZ4 (lz4 crate)

Encryption:

AES-GCM (aes-gcm crate)
ChaCha20-Poly1305 (chacha20poly1305 crate)

Hashing:

SHA-256/SHA-512 (sha2 crate)
BLAKE3 (blake3 crate)

Testing:

Criterion (benchmarking)
Proptest (property testing)
Mockall (mocking)

6.3 Development Tools

Cargo: Build system and package manager
Clippy: Linter
Rustfmt: Code formatter
Cargo-audit: Security auditing
Cargo-deny: Dependency validation
mdBook: Documentation generation

7. Design Patterns

7.1 Repository Pattern

Purpose: Abstract data persistence logic from domain logic.

Implementation: See Section 3.3

Benefits:

Domain layer independent of storage mechanism
Easy to swap SQLite for PostgreSQL or other storage
Testable with in-memory implementation

7.2 Adapter Pattern

Purpose: Integrate third-party algorithms without coupling.

Implementation: See Section 3.4

Benefits:

Easy to add new algorithms
Algorithm selection at runtime
Consistent interface across all adapters

7.3 Strategy Pattern

Purpose: Select algorithm implementation dynamically.

Example:

#![allow(unused)]
fn main() {
pub struct PipelineProcessor {
    compression_strategy: Box<dyn CompressionAdapter>,
    encryption_strategy: Box<dyn EncryptionAdapter>,
}

impl PipelineProcessor {
    pub fn new(
        compression: &str,
        encryption: &str
    ) -> Result<Self> {
        Ok(Self {
            compression_strategy: AdapterFactory::create_compression(
                compression
            )?,
            encryption_strategy: AdapterFactory::create_encryption(
                encryption
            )?,
        })
    }
}
}

7.4 Builder Pattern

Purpose: Construct complex objects step by step.

Note: Builder pattern is a potential future enhancement. Current API uses direct construction.

Current API:

#![allow(unused)]
fn main() {
let pipeline = Pipeline::new(
    "my-pipeline".to_string(),
    vec![compression_stage, encryption_stage],
)?;
}

Potential Future Builder API:

#![allow(unused)]
fn main() {
// Not yet implemented - potential future enhancement
let pipeline = Pipeline::builder()
    .name("my-pipeline")
    .add_stage(compression_stage)
    .add_stage(encryption_stage)
    .build()?;
}

7.5 Command Pattern

Purpose: Encapsulate requests as objects for undo/redo, queueing.

Example:

#![allow(unused)]
fn main() {
#[async_trait]
pub trait Command: Send + Sync {
    async fn execute(&self) -> Result<()>;
}

pub struct ProcessFileCommand {
    pipeline: Pipeline,
    input: PathBuf,
    output: PathBuf,
}

#[async_trait]
impl Command for ProcessFileCommand {
    async fn execute(&self) -> Result<()> {
        // Processing logic
    }
}
}

8. Cross-Cutting Concerns

8.1 Error Handling

Strategy: Use anyhow::Result for application errors, custom error types for domain errors.

#![allow(unused)]
fn main() {
#[derive(Debug, thiserror::Error)]
pub enum PipelineError {
    #[error("Invalid pipeline name: {0}")]
    InvalidName(String),

    #[error("No stages defined")]
    NoStages,

    #[error("Invalid chunk size: {size} (must be {min}-{max})")]
    InvalidChunkSize { size: usize, min: usize, max: usize },

    #[error("Stage order conflict at position {0}")]
    StageOrderConflict(usize),

    #[error("Unsupported algorithm: {0}")]
    UnsupportedAlgorithm(String),

    #[error("I/O error: {0}")]
    Io(#[from] std::io::Error),
}
}

8.2 Logging

Strategy: Structured logging with tracing.

#![allow(unused)]
fn main() {
use tracing::{info, warn, error, debug, instrument};

#[instrument(skip(self, data))]
async fn process_chunk(&self, chunk_id: usize, data: &[u8])
    -> Result<Vec<u8>>
{
    debug!(chunk_id, size = data.len(), "Processing chunk");

    let start = Instant::now();
    let result = self.compress(data).await?;

    info!(
        chunk_id,
        original_size = data.len(),
        compressed_size = result.len(),
        duration_ms = start.elapsed().as_millis(),
        "Chunk compressed"
    );

    Ok(result)
}
}

8.3 Metrics Collection

Strategy: Prometheus-style metrics.

#![allow(unused)]
fn main() {
pub struct PipelineMetrics {
    pub chunks_processed: AtomicUsize,
    pub bytes_processed: AtomicU64,
    pub compression_ratio: AtomicU64,
    pub processing_duration: Duration,
}

impl PipelineMetrics {
    pub fn record_chunk(&self, original_size: usize, compressed_size: usize) {
        self.chunks_processed.fetch_add(1, Ordering::Relaxed);
        self.bytes_processed.fetch_add(
            original_size as u64,
            Ordering::Relaxed
        );

        let ratio = (compressed_size as f64 / original_size as f64 * 100.0)
            as u64;
        self.compression_ratio.store(ratio, Ordering::Relaxed);
    }
}
}

8.4 Security

Key Management:

Keys never logged or persisted unencrypted
Argon2 key derivation from passwords
Secure memory wiping for key material

Input Validation:

CLI inputs sanitized against injection
File paths validated
Chunk sizes bounded

Algorithm Selection:

Only vetted, well-known algorithms
Default to secure settings
AEAD ciphers for authenticated encryption

9. Performance Considerations

9.1 Asynchronous I/O

Rationale: File I/O is the primary bottleneck. Async I/O allows overlap of CPU and I/O operations.

Implementation:

Tokio async runtime
tokio::fs for file operations
tokio::io::AsyncRead and AsyncWrite traits

9.2 Parallel Processing

Chunk-level Parallelism:

#![allow(unused)]
fn main() {
use rayon::prelude::*;

pub fn process_chunks_parallel(
    chunks: Vec<Chunk>,
    processor: &ChunkProcessor,
) -> Result<Vec<ProcessedChunk>> {
    chunks
        .par_iter()
        .map(|chunk| processor.process(chunk))
        .collect()
}
}

9.3 Memory Management

Strategies:

Fixed chunk size limits peak memory
Streaming processing avoids loading entire files
Buffer pooling for frequently allocated buffers
Zero-copy where possible

9.4 Algorithm Selection

Performance Profiles:

Algorithm	Speed	Ratio	Memory
LZ4	★★★★★	★★☆☆☆	★★★★★
Zstd	★★★★☆	★★★★☆	★★★★☆
Gzip	★★★☆☆	★★★☆☆	★★★★☆
Brotli	★★☆☆☆	★★★★★	★★★☆☆

10. Testing Strategy (Overview)

10.1 Test Organization

Unit Tests: #[cfg(test)] modules in source files
Integration Tests: tests/integration/
E2E Tests: tests/e2e/
Benchmarks: benches/

10.2 Test Coverage Goals

Domain layer: 90%+ coverage
Application layer: 80%+ coverage
Infrastructure: 70%+ coverage (mocked external deps)

10.3 Testing Tools

Mockall for mocking
Proptest for property-based testing
Criterion for benchmarking
Cargo-tarpaulin for coverage

11. Future Enhancements

11.1 Planned Features

Distributed Processing: Process files across multiple machines
Cloud Integration: S3, Azure Blob, GCS support
REST API: HTTP API for remote processing
Plugin System: Dynamic loading of custom stages
Web UI: Browser-based configuration and monitoring

11.2 Architectural Evolution

Phase 1 (Current): Single-machine CLI application

Phase 2: Library + CLI + REST API

Phase 3: Distributed processing with coordinator nodes

Phase 4: Cloud-native deployment with Kubernetes

12. Conclusion

This Software Design Document describes a robust, extensible file processing pipeline built on solid architectural principles. The design prioritizes:

Correctness through type safety and domain-driven design
Performance through async I/O and parallel processing
Maintainability through clean architecture and separation of concerns
Extensibility through ports & adapters and plugin architecture

The implementation follows Rust best practices and leverages the language's strengths in safety, concurrency, and zero-cost abstractions.

Appendix A: Glossary

Term	Definition
Aggregate	Domain entity that serves as consistency boundary
AEAD	Authenticated Encryption with Associated Data
Chunk	Fixed-size portion of file for streaming processing
DDD	Domain-Driven Design architectural approach
Port	Interface defined by application core
Adapter	Implementation of port for external system
Use Case	Application-specific business rule
Value Object	Immutable object defined by its attributes

Appendix B: Diagrams

See the following chapters for detailed diagrams:

Pipeline Developer Guide